1. Field of the Invention
The subject matter disclosed relates generally to the location and entry of a lateral hydrocarbon well from a main wellbore in a subterranean formation. More particularly, the subject matter disclosed relates to a robot capable of identifying unknown surfaces in a wellbore.
2. Background of the Invention
Multilateral hydrocarbon wells i.e. hydrocarbon wells having one or more secondary wellbores connecting to a main wellbore, are common in the oil industry. Location, or location and entry of one or more of the secondary or lateral wellbores, whether in completion or treatment procedures for a new well, or for reconditioning or reworking of an older well often can pose as a problem for the well service operator.
In addition, world oil demand and advance recovery techniques have made it economically attractive to rehabilitate previously abandoned oil wells. Rehabilitating requires lowering instruments and tools into the wells. These wells often have a number of junctions where divergent branches leave the main well at unrecorded depths. These junctions were not intended to be re-entered after their construction. To rehabilitate a divergent branch, the location and shape of its junction must be determined. The data acquisition to map a junction must be completed quickly given the high cost of keeping a well out of service.
Well mapping is challenging because the opaque fluids that fill the well to avoid its collapse prevent the use of visual sensors to measure the junction. Frequently, a layer of “mud cake” often obscures the well bore surface.
Past research on tactile characterization of unknown geometries has considered a number of approaches. In an early study, a tactile exploration technique for locating and identifying a 2D object among a library of known objects is developed (Schneiter, J. “Automated Tactile Sensing for Object Recognition and Localization. Ph.D. Thesis, Department of Mechanical Engineering, MIT, 1986). In this work, a tree of object identity hypotheses is made and the search for the next data point is selected to maximize the potential of pruning this tree. The method has been extended to 3D polygonal objects (Roberts, K., “Robot active touch exploration: constraints and strategies.” Proc. IEEE Int. Conf. Robotics and Automation 980-985, 1990). This method cannot handle unknown geometries because it relies on a library of specific objects.
Approaches for general, unknown objects have been developed. A common approach is based on the description of a surface with a mesh. (Caselli et al., “Efficient Exploration and recognition of convex objects based on haptic perception”, Proc. IEEE Int. Conf. Robotics and Automation 3508-3513, 1996 and Chew, L., “Guaranteed-quality mesh generation for curved surfaces”, Proc. Ninth annual Symposium on Computational Geometry, 274-280, 1993). This can also be used with a tree search for object recognition. (Beccari et al., “Pose-independent recognition of convex objects from sparse tactile data”, Proc. IEEE Int. Conf. Robotics and Automation 3397-3402, 1997). While a mesh is an effective representation of a general surface, it requires dense data and it is therefore not applicable for sparse tactile data problems. An alternative approach represents surface geometry as a composition of geometric primitives, such as planes, cylinders, and spheres. These primitives are often determined with curve and surface fitting methods. (Allen et al., “Acquisition and interpretation of 3-D sensor data from touch”, IEEE Trans. Robotics and Automation 6(4): 397-404, 1990 and Pribade et al., “Exploration and dynamic shape estimation by a robotic probe”, IEEE Trans. Systems, Man and Cybernetics 19(4): 840-846, 1989). Alternatively, they can be determined using differential invariants. (Keren et al., “Recognizing 3D objects using tactile sensing and curve invariants”, J. Mathematical Imaging and Vision 12(1), 5-23, 2000). In this particular approach, when a series of grid points are found to belong to the same fitted curve or surface, the spacing between subsequent data points is increased. This method is still tied to the grid sampling concept and therefore inherently uses dense data.
All of the methods developed for both an intelligent exploration and the characterization of general unknown geometries have not been integrated to achieve fast geometry characterization with sparse data. The present invention address the problems of the prior art, in particular, the general problem of intelligent tactile exploration of constrained internal geometries where time is a key factor.